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Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota

  • Applied genetics and molecular biotechnology
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Abstract

One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

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Acknowledgements

This work was supported by the Project of Science and Technology Department of Ningbo (2017C10044), the Zhejiang Province Public Welfare Technology Application Research Project (2016C32063), and the K.C. Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. Faculty of Marine Science, Ningbo University, Ningbo, 315211, China

    Jinbo Xiong, Weina Yu, Wenfang Dai, Jinjie Zhang, Qiongfen Qiu & Changrong Ou

  2. Collaborative Innovation Center for Zhejiang Marine High-Efficiency and Healthy Aquaculture, Ningbo, 315211, China

    Jinbo Xiong, Weina Yu & Wenfang Dai

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  1. Jinbo Xiong
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  2. Weina Yu
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  3. Wenfang Dai
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Correspondence to Jinbo Xiong.

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This article does not contain any studies with human participants performed by any of the authors. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

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Xiong, J., Yu, W., Dai, W. et al. Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota. Appl Microbiol Biotechnol 102, 3315–3326 (2018). https://doi.org/10.1007/s00253-018-8874-z

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